Flexible enhanced fuzzy min–max neural network model for pattern classification problems

In the attempts of building an efficient classifier model, various hybrid computational intelligence models have been introduced. Among these, the enhanced fuzzy min-max (EFMM) model was one of the most recent models coming with many essential features like the ability to provide online learning pro...

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Main Author:	Al-Hroob, Essam Muslem Harb
Format:	Thesis
Language:	English
Published:	2020
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/30400/1/Flexible%20enhanced%20fuzzy%20min%E2%80%93max%20neural%20network%20model.pdf
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spelling	my-ump-ir.304002020-12-31T14:43:48Z Flexible enhanced fuzzy min–max neural network model for pattern classification problems 2020-06 Al-Hroob, Essam Muslem Harb QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering In the attempts of building an efficient classifier model, various hybrid computational intelligence models have been introduced. Among these, the enhanced fuzzy min-max (EFMM) model was one of the most recent models coming with many essential features like the ability to provide online learning processes and handling the forgetting problem. Although EFMM has been proven to be one of the most premier models for undertaking the pattern classification problems, issues related to its learning process, concerning the overlap between the hyperboxes, random expansion coefficient value (user-defined) and hyperbox contraction remain unsolved. Therefore, two stages of improvements are introduced in this research to overcome the current limitations and improve classification performance in terms of accuracy and complexity. In the first stage, a new flexible enhanced fuzzy min-max (FEFMM) model is proposed to overcome limitations related to accuracy issue. Hence, four new procedures are introduced. First, a new training strategy to avoid generating unnecessary overlapped regions. Second, a new flexible expansion procedure to replace the expansion coefficient user-defined parameter with a self-adaptive value to produce more accurate decision boundaries. Third, a new overlap test rule is applied during the testing phase to identify any possible containment overlap case and activate the contraction process (if necessary). Fourth, a new contraction procedure to overcome the containment overlap and avoiding the data distortion problem (missing hyperbox information). In the second stage, a new pruning strategy is proposed to further enhance the performance of the proposed model in regards to overcome the network complexity problem. Hence, the resulting model is known as FEFMM-based pruning strategy (FEFMM-PS). The usefulness of both stages is evaluated systematically using a series of experiments using several benchmark datasets. Sixteen data sets are used in the evaluation process. These data sets are obtained from the UCI machine learning repository and the selection of these data sets is related to cover examples of different levels of difficulties, input and output classes, features, and a number of instances. The performance of FEFMM-PS in these experiments are then quantified using statistical measures where the bootstrap and k-fold cross-validation methods have been adopted. The results demonstrate the efficiency of FEFMM in handling pattern classification problems and providing a superior performance of classification accuracy as compared to the other network structures from the same variants such as EFMM, FMM variants and also non-FMM related models. Concerning the FEFMM-PS, the finding reveals that the model (FEFMM-PS) is able to solve network complexity problem and presents better classification accuracy as compared to FEFMM and other models from the literature. The proposed models FEFMM and FEFMM-PS can be applied in several application areas to further assess their applicability, such as face recognition, speaker recognition, signature recognition, and text classification. 2020-06 Thesis http://umpir.ump.edu.my/id/eprint/30400/ http://umpir.ump.edu.my/id/eprint/30400/1/Flexible%20enhanced%20fuzzy%20min%E2%80%93max%20neural%20network%20model.pdf pdf en public phd doctoral Universiti Malaysia Pahang Faculty of Computing
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	English
topic	QA76 Computer software QA76 Computer software
spellingShingle	QA76 Computer software QA76 Computer software Al-Hroob, Essam Muslem Harb Flexible enhanced fuzzy min–max neural network model for pattern classification problems
description	In the attempts of building an efficient classifier model, various hybrid computational intelligence models have been introduced. Among these, the enhanced fuzzy min-max (EFMM) model was one of the most recent models coming with many essential features like the ability to provide online learning processes and handling the forgetting problem. Although EFMM has been proven to be one of the most premier models for undertaking the pattern classification problems, issues related to its learning process, concerning the overlap between the hyperboxes, random expansion coefficient value (user-defined) and hyperbox contraction remain unsolved. Therefore, two stages of improvements are introduced in this research to overcome the current limitations and improve classification performance in terms of accuracy and complexity. In the first stage, a new flexible enhanced fuzzy min-max (FEFMM) model is proposed to overcome limitations related to accuracy issue. Hence, four new procedures are introduced. First, a new training strategy to avoid generating unnecessary overlapped regions. Second, a new flexible expansion procedure to replace the expansion coefficient user-defined parameter with a self-adaptive value to produce more accurate decision boundaries. Third, a new overlap test rule is applied during the testing phase to identify any possible containment overlap case and activate the contraction process (if necessary). Fourth, a new contraction procedure to overcome the containment overlap and avoiding the data distortion problem (missing hyperbox information). In the second stage, a new pruning strategy is proposed to further enhance the performance of the proposed model in regards to overcome the network complexity problem. Hence, the resulting model is known as FEFMM-based pruning strategy (FEFMM-PS). The usefulness of both stages is evaluated systematically using a series of experiments using several benchmark datasets. Sixteen data sets are used in the evaluation process. These data sets are obtained from the UCI machine learning repository and the selection of these data sets is related to cover examples of different levels of difficulties, input and output classes, features, and a number of instances. The performance of FEFMM-PS in these experiments are then quantified using statistical measures where the bootstrap and k-fold cross-validation methods have been adopted. The results demonstrate the efficiency of FEFMM in handling pattern classification problems and providing a superior performance of classification accuracy as compared to the other network structures from the same variants such as EFMM, FMM variants and also non-FMM related models. Concerning the FEFMM-PS, the finding reveals that the model (FEFMM-PS) is able to solve network complexity problem and presents better classification accuracy as compared to FEFMM and other models from the literature. The proposed models FEFMM and FEFMM-PS can be applied in several application areas to further assess their applicability, such as face recognition, speaker recognition, signature recognition, and text classification.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Al-Hroob, Essam Muslem Harb
author_facet	Al-Hroob, Essam Muslem Harb
author_sort	Al-Hroob, Essam Muslem Harb
title	Flexible enhanced fuzzy min–max neural network model for pattern classification problems
title_short	Flexible enhanced fuzzy min–max neural network model for pattern classification problems
title_full	Flexible enhanced fuzzy min–max neural network model for pattern classification problems
title_fullStr	Flexible enhanced fuzzy min–max neural network model for pattern classification problems
title_full_unstemmed	Flexible enhanced fuzzy min–max neural network model for pattern classification problems
title_sort	flexible enhanced fuzzy min–max neural network model for pattern classification problems
granting_institution	Universiti Malaysia Pahang
granting_department	Faculty of Computing
publishDate	2020
url	http://umpir.ump.edu.my/id/eprint/30400/1/Flexible%20enhanced%20fuzzy%20min%E2%80%93max%20neural%20network%20model.pdf
_version_	1783732146009014272

Flexible enhanced fuzzy min–max neural network model for pattern classification problems

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